| Autor: |
X.Y. Xie, H.L. Xu, Q.Y. Li, Y.J. Sun |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Journal of Instrumentation. 16:P12002 |
| ISSN: |
1748-0221 |
| DOI: |
10.1088/1748-0221/16/12/p12002 |
| Popis: |
A data-based machine learning approach is proposed to study the properties of time resolution of RPC detectors by measuring the time of flight of cosmic muons. This method utilises a multi-layer perceptron and a type of recurrent neural network called long short-term memory. The neural network is trained with the waveforms of RPC signals digitized by an oscilloscope at a sampling frequency of 10 GHz and a 2 GHz bandwidth. A data augmentation approach is implemented for labelling. Compared to the results from conventional waveform analysis, this approach achieves a better time resolution of 1-mm gap RPCs. Based on the data, the approach has a generalisation capacity for performance studies of other timing detectors. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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